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Comment by cromwellian | Hacker News Reader

Also, it cherry picks examples of failed startups or ideas, but ignores all of the ML that Google did turn into viable end user experiences: Smart Reply, WaveNet, Translate, Speech Recognition, RankBrain, Photo/Image Search, Retinal diagnosis, Waymo (soon), and numerous other components that are used in Android and the Assistant.

The whole point of venture capital or R&D is that you don't know what will work, and expect 40 failures for 1 success. She seems to think that the way science progresses is that you iterate on a Phd thesis until it's "Done" and verified and then it becomes applied. But most Phd theses, even though that survive peer review, end up inapplicable, unused, or forgotten. Not everyone publishes a General Relativity paper. A ton of Phd papers are junk, a good number don't even have reproducible results.

The only way to know if someone will be successful is to try it and let it succeed or fail in the marketplace.

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> Smart Reply, WaveNet, Translate, Speech Recognition, RankBrain, Photo/Image Search, Retinal diagnosis, Waymo (soon), and numerous other components that are used in Android and the Assistant.

These aren't examples of just productionizing a PhD thesis - they're thoughtfully designed products that solve a real problem.

Unfortunately we've watched as many ML PhD graduates launch startups which are little more than an API wrapped around the key algorithm from their thesis. These startups nearly always fail, because they don't actually address a market need.

> The only way to know if someone will be successful is to try it and let it succeed or fail in the marketplace.

There are many ways to estimate potential market size, product-market fit, etc ahead of time. They're not perfect, but they're a lot better than nothing.

> These aren't examples of just productionizing a PhD thesis

His point is that sometimes research bears fruit, and sometimes it doesn't. All universities (in my country) now have metrics for how much research must successfully bear commercial-fruit or else they lose funding from the government and the EU. As such, they have commercialisation pipelines that funnel viable commercial research into products. However, even these funding bodies don't expect that anything more than a fraction of research will be commercialisable. That isn't the point of research.

> we've watched as many ML PhD graduates launch startups which are little more than an API wrapped around the key algorithm from their thesis. These startups nearly always fail, because they don't actually address a market need.

Why do you care what someone does with their PhD research anyway?

Without PhDs work, you have no clue where to look. Pushing the idea to market not only requires a market fit, but also requires BS everywhere to educate customers. Building a product are efforts, but it is as equal as the efforts of creating the idea.

> Photo/Image Search

It’s nice but … I still have to scroll about 15 pages into “cat” to see a picture which isn’t my dog. I’m receptive to the argument that this is more an advertising/ positioning move than a major advance.

Can you share a picture of your dog?

Disclosure: I do work for Google Cloud, but all I'm here for is to see if that dog does look like a cat.

Here's an example: https://www.dropbox.com/s/uvaara0pbelgj4q/IMG_6503.PNG?dl=0

I'd bet it trained on something like the fur texture since it also matched things like a lemur which have longer smooth fur.

Really? I’ve never ever seen a problem that bad with it.

Dog vs Cat is one of the best studied problems in deep learning, and there is lots of training data. This is very surprising!

Here's an example: https://www.dropbox.com/s/uvaara0pbelgj4q/IMG_6503.PNG?dl=0

I'd bet it trained on something like the fur texture since it also matched things like a lemur which have longer smooth fur.

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